465 citations · 4.1k across the 97 of their papers we have counts for
102 papers · 1 filter
Reward Uncertainty for Exploration in Preference-based Reinforcement Learning
Xinran Liang, Katherine Shu, Kimin Lee +1
Conveying complex objectives to reinforcement learning (RL) agents often requires meticulous reward engineering. Preference-based RL methods are able to learn a more flexible rewar…
Chain of Thought Imitation with Procedure Cloning
Mengjiao Yang, Dale Schuurmans, Pieter Abbeel +1
Imitation learning aims to extract high-performance policies from logged demonstrations of expert behavior. It is common to frame imitation learning as a supervised learning proble…
An Empirical Investigation of Representation Learning for Imitation
Xin Chen, Sam Toyer, Cody Wild +9
Imitation learning often needs a large demonstration set in order to handle the full range of situations that an agent might find itself in during deployment. However, collecting e…
Imitating, Fast and Slow: Robust learning from demonstrations via decision-time planning
Carl Qi, Pieter Abbeel, Aditya Grover
The goal of imitation learning is to mimic expert behavior from demonstrations, without access to an explicit reward signal. A popular class of approach infers the (unknown) reward…
Pretraining Graph Neural Networks for few-shot Analog Circuit Modeling and Design
Kourosh Hakhamaneshi, Marcel Nassar, Mariano Phielipp +2
Being able to predict the performance of circuits without running expensive simulations is a desired capability that can catalyze automated design. In this paper, we present a supe…
CIC: Contrastive Intrinsic Control for Unsupervised Skill Discovery
Michael Laskin, Hao Liu, Xue Bin Peng +3
We introduce Contrastive Intrinsic Control (CIC), an algorithm for unsupervised skill discovery that maximizes the mutual information between state-transitions and latent skill vec…